import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_b = tf.matmul(inputs,Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

x_datas = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.1,x_datas.shape)
y_datas =  np.square(x_datas)-0.5 + noise

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])

l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1)

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_datas,y_datas)
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_datas,ys:y_datas})
    if i%50==0:
        print(sess.run(loss,feed_dict={xs:x_datas,ys:y_datas}))
        prediction_v = sess.run(prediction,feed_dict={xs:x_datas})
        lines = ax.plot(x_datas,prediction_v,'r-',lw=5)
        try:    
            plt.pause(0.5)
            ax.lines.remove(lines[0])
        except Exception:
            pass
    
            
            
        